SynergyWarpNet：基于注意力的协同形变用于神经肖像动画
SynergyWarpNet: Attention-Guided Cooperative Warping for Neural Portrait Animation
AI总结：
SynergyWarpNet通过基于注意力的协同形变框架，实现了高保真的说话头合成，解决了传统方法在运动转移和几何基础方面的不足。
摘要：
最近神经肖像动画的进展已展示了在虚拟化身、远程存在和数字内容创作中的巨大潜力。然而，传统的显式形变方法往往在准确的运动转移或恢复缺失区域方面存在困难，而最近的基于注意力的形变方法虽然有效，但常常面临复杂度高和几何基础弱的问题。为了解决这些问题，我们提出了SynergyWarpNet，一种基于注意力的协同形变框架，用于高保真的说话头合成。给定一个源肖像、驱动图像和一组参考图像，我们的模型分三个阶段逐步优化动画。首先，一个显式形变模块利用3D密集光流在源图像和驱动图像之间进行粗略的空间对齐。接下来，一个参考增强的校正模块利用多个参考图像中的3D关键点和纹理特征的交叉注意力，语义上完成被遮挡或扭曲的区域。最后，一个置信度引导的融合模块整合变形输出，使用学习得到的置信度图进行空间自适应融合，以平衡结构对齐和视觉一致性。在基准数据集上的全面评估显示了最先进的性能。
英文摘要：
Recent advances in neural portrait animation have demonstrated remarked potential for applications in virtual avatars, telepresence, and digital content creation. However, traditional explicit warping approaches often struggle with accurate motion transfer or recovering missing regions, while recent attention-based warping methods, though effective, frequently suffer from high complexity and weak geometric grounding. To address these issues, we propose SynergyWarpNet, an attention-guided cooperative warping framework designed for high-fidelity talking head synthesis. Given a source portrait, a driving image, and a set of reference images, our model progressively refines the animation in three stages. First, an explicit warping module performs coarse spatial alignment between the source and driving image using 3D dense optical flow. Next, a reference-augmented correction module leverages cross-attention across 3D keypoints and texture features from multiple reference images to semantically complete occluded or distorted regions. Finally, a confidence-guided fusion module integrates the warped outputs with spatially-adaptive fusing, using a learned confidence map to balance structural alignment and visual consistency. Comprehensive evaluations on benchmark datasets demonstrate state-of-the-art performance.
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